Cognitive Maps: How the Brain Organizes Knowledge Ling 411 – 18.
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Transcript of Cognitive Maps: How the Brain Organizes Knowledge Ling 411 – 18.
Cognitive Maps:How the Brain Organizes Knowledge
Ling 411 – 18
The Cognitive Map Hypothesis
Hypothesis: Knowledge is organized in the cortex as maps Established (hence not hypothetical):
• The cognitive map of the body Primary motor and somatosensory areas
• The map of pitch frequency In primary auditory area
Hypothesized:• Conceptual• Phonological
Properties of cognitive maps
Established for somatic and frequency maps• Local specificity
Every cortical location has a specific function• Adjacency
Adjacent locations for adjacent functions Nearby locations for related functions Comes in degrees
Hypothesis: these properties apply to • all homotypical cortical areas• all types of knowledge represented in the cortex
First step in exploring the hypothesis:Categories
Understanding phonology• Phonological structure is organized around
phonological categories E.g., vowels and consonants, voiceless stops
Understanding semantics• Semantic structure is largely a matter of conceptual
categories• Understanding how categories work is the key to
unlock the mysteries of semantics• To understand how categories work we need to
understand how the brain manages categorial information
What is a concept?Concepts vs. percepts
Percept: one sensory modality• Locations are known
• Auditory: temporal lobe• Visual: occipital lobe• Somatosensory: parietal lobe
Concept: more than one sensory modality• Higher level (more abstract)• Locations, for nominal concepts:
Angular gyrus (?)MTG
Types of Conceptual Categories
Discrete• Even integers• Counties in Texas
Radial• Birds• Vehicles
Family resemblance• Games• Furniture
Ill-defined• Thought• Mind
Phenomena associated with conceptual categories
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world5. Categories and their memberships vary from one
language/culture system to another6. Categories influence thinking, in both appropriate and
inappropriate ways7. Subcategories, and sub-subcategories, in hierarchical
chains
Phenomena associated with categories: 1
1. No small set of defining features (with rare exceptions)
• The feature-attribute model fails Works for some mathematical objects, but
doesn’t apply to the way people’s cognitive systems apprehend most things
Example: CUP
Phenomena associated with categories: 2
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries
• Example: VEHICLE Car, truck, bus Airplane? Boat? Toy car, model airplane? Raft? Roller skate? Snowboard?
Fuzzy Categories
No fixed boundaries Membership comes in degrees
• Prototypical • Less prototypical• Peripheral• Metaphorical
The property of fuzziness relates closely to the phenomenon of prototypicality
Phenomena associated with categories: 3
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members
• Prototypical CAR, TRUCK, BUS
• Peripheral: AIRPLANE, TOY CAR, RAFT, ROLLER SKATE, etc.
• Varying degrees of peripherality
Prototypicality phenomena
The category BIRD
• Some members are prototypical ROBIN, SPARROW
• Others are peripheral EMU, PENGUIN
The category VEHICLE• Prototypical: CAR, TRUCK, BUS
• Peripheral: ROLLER SKATE, HANG GLIDER
Phenomena associated with categories: 4
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members
4. Categories are in the mind, not in the real world
• In the world, everything is unique lacks clear boundaries changes from day to day (even moment to
moment)• Whorf: “kaleidoscopic flux”
Phenomena associated with categories: 51. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world
5. Categories and their memberships vary from one language/culture system to another
cloche (of a church)clochette (on a cow)sonnette (of a door)grelot (of a sleigh)timbre (on a desk)glas (to announce a death)
English: French:
bell
Phenomena associated with categories - 6
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world5. Categories and their memberships vary from one language/culture
system to another
6. Categories influence thinking, in both appropriate and inappropriate ways
• B.L. Whorf• Example: Racial profiling
Phenomena associated with categories - 7
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world5. Categories and their memberships vary from one language/culture
system to another6. Categories influence thinking, in both appropriate and
inappropriate ways
7. Subcategories, and sub-subcategories, in hierarchical chains
• ANIMAL – MAMMAL – CARNIVORE – CANINE – DOG –TERRIER – JACK RUSSELL TERRIER – EDDIE
• Each subcategory has the properties of the category plus additional properties
• Smallest subcategory has the most properties
Beyond description to explanation
How can we explain these phenomena? The answer this question depends on how our
information about categories is represented in the brain The brain is where our linguistic and cultural knowledge
is represented
Facts and a hypothesis that we can build on
Fact: The brain is a network• Composed, ultimately, of neurons• Cortical neurons are clustered in columns
Columns come in different sizes Each minicolumn acts as a unit
Therefore a person’s linguistic and conceptual system is a network
Hypothesis: Every word and every concept is represented as a sub-network• Term: functional web (Pulvermüller 2002)
Properties of functional webs
I: Functional Webs• A concept is represented as a functional web
II: Columnar Nodes• Nodes are implemented as cortical columns
III: Nodal Specificity • Every node in a functional web has a specific function
III(a): Adjacency• Nodes of related function are in adjacent locations
More closely related function, more closely adjacent
Property III(a): Adjacency
Nodes of related function are in adjacent locations• More closely related function, more closely adjacent
Examples:• Adjacent locations on cat’s paw represented by
adjacent cortical locations• Similar line orientations represented by adjacent
cortical locations
Hypotheses concerning conceptual webs
Hypothesis I: Extrapolation to Humans• The findings about cortical structure and function
from experiments on cats, monkeys, and rats can be extrapolated to humans
• Hypothesis I(a): The extrapolation can be extended to linguistic and conceptual structures and functions
Hypothesis II: Hierarchy • A functional web is hierarchically organized
Hypothesis III: Cardinal nodes• Every functional web has a cardinal node • Hypothesis III(a):
Each subweb likewise has a cardinal node
(Part of) the functional web for CAT
V
P
A
M
C
The cardinal node for the entire functional web
T
Cardinal nodes of the subwebs
Phenomena associated with categories
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Subcategories, and sub-subcategories, in hierarchical
chains5. Categories are in the mind, not in the real world6. Categories and their memberships vary from one
language/culture system to another7. Categories influence thinking, in both appropriate and
inappropriate ways
REVIEW
How to explain?
Description is fine, but its only a start Next step: Explanation How to explain?
• By answering the question of how categories are represented in the brain
REVIEW
Phenomena associated with categories: 1-3
1. No small set of defining features (with rare exceptions) • Example: CUP
• More realistic alternative: radial categories2. Fuzzy boundaries
• Example: VEHICLE
3. Prototypical members and peripheral members• VEHICLE
Prototypical:• CAR, TRUCK, BUS
Peripheral: • AIRPLANE, TOY CAR, RAFT, ROLLER SKATE, etc.• Varying degrees of peripherality
These three phenomena are interdependent
How do radial categories work?
Different connections have different strengths (weights) More important properties have greater strengths For CUP,
• Important (but not necessary!) properties: Short (as compared with a glass) Ceramic Having a handle
Cups with these properties are more prototypical
The properties of a category have different weights
T
CUP
MADE OF GLASS
CERAMIC
SHORT
HAS HANDLE
The properties are represented by nodes which are connected to lower-level nodes
The cardinal node
The threshold
More important properties have greater weights, represented by greater thicknesses of lines
Activation of a category node
The node will be activated by any of many different combinations of properties
The key word is enough – it takes enough activation from enough properties to satisfy the threshold
The node will be activated to different degrees by different combinations of properties• When strongly activated, it transmits stronger
activation to its downstream nodes.
Prototypical exemplars provide stronger and more rapid activation
T
CUP
MADE OF GLASS
CERAMIC
SHORT
HAS HANDLE
Stronger connections carry more activation
Activation threshold (can be satisfied to varying degrees)
Inhibitory connection
Explaining Prototypicality
Cardinal category nodes get more activation from the prototypical exemplars • More heavily weighted property nodes
E.g., FLYING is strongly connected to BIRD • Property nodes more strongly activated
Peripheral items (e.g. EMU) provide only weak activation, weakly satisfying the threshold (emus can’t fly)
Borderline items may or may not produce enough activation to satisfy threshold
Activation of different sets of properties produces greater or lesser satisfaction of the activation threshold of the cardinal node
CUP
MADE OF GLASS
CERAMICSHORT
HAS HANDLE
Explaining prototypicality: Summary
Variation in strength of connections Many connecting properties of varying strength Varying degrees of activation Prototypical members receive stronger activation from
more associated properties BIRD is strongly connected to the property FLYING
• Emus and ostriches don’t fly• But they have some properties connected with BIRD• Sparrows and robins do fly
And as commonly occurring birds they have been experienced often, leading to entrenchment – stronger connections
Phenomena associated with categories: 4
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members
4. Categories are in the mind, not in the real world• In the world, everything
is unique lacks clear boundaries changes from day to day (even moment to
moment)• Whorf: “kaleidoscopic flux”
Phenomena associated with categories: 51. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world
5. Categories and their memberships vary from one language/culture system to another
cloche (of a church)clochette (on a cow)sonnette (of a door)grelot (of a sleigh)timbre (on a desk)glas (to announce a death)
English: French:
bell
REVIEW
Phenomena associated with categories - 6
1. No small set of defining features (with rare exceptions) 2. Fuzzy boundaries3. Prototypical members and peripheral members4. Categories are in the mind, not in the real world5. Categories and their memberships vary from one language/culture
system to another
6. Categories influence thinking, in both appropriate and inappropriate ways
• B.L. Whorf• Example: Racial profiling
These phenomena (4-6) are interrelated
4. Categories are in the mind, not in the real world5. Categories and their memberships vary from one
language/culture system to another6. Categories influence thinking, in both appropriate and
inappropriate ways• B.L. Whorf• Example: Racial profiling
Bidirectional processing and inference
T
CUP
MADE OF GLASS
CERAMICSHORT
HANDLE
These connections are bidirectional
Separate fibers for the two directions; shown as one line in the notation
Bidirectional processing and inference
T
CUP
SHORT
HANDLE
Thought process: 1. The cardinal concept node is activated by a subset of its property nodes 2. Feed-backward processing activates other property nodes
Consequence: We “apprehend” properties that are not actually perceived
Another hypothesis of Whorf
Grammatical categories of a language influence the thinking of people who speak the language
Can we explain this too in terms of brain structure?
Example: Grammatical gender
Does talking about inanimate objects as if they were masculine or feminine actually lead people to think of inanimate objects as having a gender?
Could the grammatical genders assigned to objects by a language influence people’s mental representation of objects?
Boroditsky (2003)
Experiment: Gender and Associations(Boroditsky et al. 2002)
Subjects: speakers of Spanish or German• All were fluent also in English• English used as language of experiment
Task: Write down the 1st 3 adjectives that come to mind to describe each object• All the (24) objects have opposite gender
in German and Spanish Raters of adjectives: Native English speakers
Examples:
Key (masc in German, fem in Spanish)• Adjectives used by German speakers:
Hard, heavy, jagged, metal, serrated, useful• Adjectives used by Spanish speakers:
Golden, intricate, little, lovely, shiny, tiny Bridge (fem in German, masc in spanish)
• Adjectives used by German speakers: Beautiful, elegant, fragile, peaceful, pretty
• Adjectives used by Spanish speakers: Big, dangerous, long, strong, sturdy, towering
Results of the Experiment(Boroditsky et al. 2002)
Raters of adjectives were native English speakers Result: Adjectives were rated as masculine or feminine
in agreement with the gender in subject’s native language
Categories and the brain
All of these phenomena associated with categories can be explained as inevitable consequences of the structure and function of the human brain
Phenomena associated with categories: 7
7. Subcategories, and sub-subcategories, in hierarchical chains
• ANIMAL – MAMMAL – CARNIVORE – CANINE – DOG –TERRIER – JACK RUSSELL TERRIER – EDDIE
• Each subcategory has the properties of the category plus additional properties
• Smallest subcategory has the most properties
How to explain? Perceptual Neuroscience
Hypothesis I: Extrapolation• The findings described by Mountcastle can be
extrapolated to humans Hypothesis I(a): Extrapolation can be extended to
linguistic and conceptual structures Why? Cortical structure, viewed locally, is
• Uniform across mammalian species • Uniform across different cortical regions
Cortical structure and function, locally, are essentially the same in humans as in cats and monkeys and rats• Moreover, in humans, the regions that support language have
the same structure locally as other cortical regions
Support for the extrapolation hypothesis
Conceptual systems in humans evidently use the same structures as perceptual systems
Therefore it is not too great a stretch to suppose that experimental findings on the structure of perceptual systems in monkeys can be applied to an understanding of the structure of conceptual systems of human beings
In particular to the structures of conceptual categories
REVIEW
Columns of different sizes
Minicolumn• Basic anatomically described unit• 70-110 neurons (avg 75-80)• Diameter barely more than that of pyramidal cell body (30-50 μ)
Maxicolumn (term used by Mountcastle)• Diameter 300-500 μ• Bundle of about 100 continuous minicolumns
Hypercolumn – up to 1 mm diameter• Can be long and narrow rather than cylindrical
Functional column• Intermediate between minicolumn and maxicolumn• A contiguous group of minicolumns
Functional Columns
Intermediate in size between minicolumn and maxicolumn
Hypothesized functional unit whose size is determined by experience/learning
A maxicolumn consists of multiple functional columns A functional column consists of multiple minicolumns Functional column may be further subdivided with
learning of finer distinctions
Columns of different sizes In order according to size
Minicolumn• The smallest unit• 70-110 neurons
Functional column• Variable size – depends on experience• Intermediate between minicolumn and maxicolumn
Maxicolumn (a.k.a. column)• 100 to a few hundred minicolumns
Hypercolumn• Several contiguous maxicolumns
Hypercolums: Modules of maxicolumns
A visual area in temporal lobe of a macaque monkey
Perceptual subcategories andcolumnar subdivisions of larger columns
Nodal specificity applies for maxicolumns as well as for minicolumns
The adjacency hypothesis likewise applies to larger categories and columns• Adjacency applies for adjacent maxicolumns
Subcategories of a category have similar function• Therefore their cardinal nodes should be in adjacent
locations
Functional columns
The minicolumns within a maxicolumn respond to a common set of features
Functional columns are intermediate in size between minicolumns and maxicolumns
Different functional columns within a maxicolumn are distinct because of non-shared additional features • Shared within the functional column• Not shared with the rest of the maxicolumn
Mountcastle: “The neurons of a [maxi]column have certain sets of static and dynamic properties in common, upon which others that may differ are superimposed.”
Similarly..
Neurons of a hypercolumn may have similar response features, upon which others that differ may be superimposed
Result is maxicolumns in the hypercolumn sharing certain basic features while differing with respect to others
Such maxicolumns may be further subdivided into functional columns on the basis of additional features
That is, columnar structure directly maps categories and subcategories
Hypercolums: Modules of maxicolumns
A visual area in the temporal lobe of a macaque monkey
Category (hypercolumn)
Subcategory(can be further subdivided)
Category representations in the cortex
Hypercolumn
Maxicolumn
Functional column
Sub-functional column
Supercategory
Category
Subcategory
Sub-subcategory
Hypothesis applied to conceptual categories
A whole maxicolumn gets activated for a category• Example: BEAR
Different functional columns within the maxicolumn for subcategories
• BROWN BEAR, GRIZZLY, POLAR BEAR, etc.
Adjacent maxicolumns for categories related to BEAR (sharing various features)• I.e. , other carnivores
Similarly, CUP has a column surrounded by columns for other drinking vessels
Perceptual subcategories andcolumnar subdivisions of larger columns
Nodal specificity applies for maxicolumns as well as for minicolumns
The adjacency hypothesis likewise applies to larger categories and columns• Adjacency applies for adjacent maxicolumns
Subcategories of a category have similar function• Therefore their cardinal nodes should be in adjacent
locations
Support from patients with brain damage(from Rapp & Caramazza 1995)
J.B.R. and S.B.Y. (905b-906a)
Herpes simplex encephalitis Both temporal lobes affected Could not define animate objects
• ostrich, snail, wasp, duck, holly Much better at defining inanimate objects
tent, briefcase, compass, wheelbarrow, submarine, umbrella
Conclusion: cortical areas for conceptual categories
Additional support from cases of brain damage
J.J. and P.S. (Hillis & Caramazza 1991) (906-7)• J.J. – left temporal, basal ganglia (CVA)
Selective preservation of animal concepts• P.S. – mostly left temporal (injury)
Selective impairment of animate category
P.S
J.J.
Two different patients with anomia
Deficit in retrieval of animal names(Damage from stroke)
Inability to retrieve words for unique entities(Left temporal lobectomy)
Two more patients with anomia
Deficit in retrieval of words for man-made manipulable objects(Damage from stroke)
Severe deficit in retrieval of words for concrete entities(Herpes simplex encephalitis)
What is it that determines location?
Logical categories like ANIMALS vs. TOOLS/UTENSILS?• If so, why?
Abstract categories based on cognitively salient properties?
Animals vs. Tools/Utensils?
These two categories have been studied most extensively in the literature
What is it that determines location? Observations:
• Most animals are known mostly in the visual modality• Many tools and utensils are known largely in the
somatosensory and motor modalities
Proximity principle and nominal concepts
Supramarginal gyrus, angular gyrus, and middle temporal gyrus are all close to Wernicke’s area
Angular gyrus occupies intermediate location between the major perceptual modalities
Supramarginal gyrus especially close to somatosensory perception
Middle temporal gyrus especially close to visual perception
Functional columns in phonological recognitionA hypothesis
Demisyllable (e.g. /de-/) activates a maxicolumn Different functional columns within the maxicolumn
for syllables with this demisyllable• /ded/, /deb/, /det/, /dek/, /den/, /del/
Demisyllables [di, de, da, du]
F1 and F2For [de]
It is unlikely that [d] is represented as a unit in perception
Functional columns in phonological recognitionA hypothesis
[de-]
A maxicolumn (ca. 100 minicolumns)
Divided into functional columns
(Note that all respond to /de-/)
deb
ded den de- det del
dek
Phonological hypercolumns (a hypothesis)
Maybe we have • Hypercolumn of contiguous maxicolumns for /e/• With maxicolumns for /de-/, /be-/, etc.• Each such maxicolumn subdivided into functional
columns for different finals /det/, /ded/, /den/, /deb/, /dem/. /dek/
N.B.: This is a hypothesis, not proven• But there is indirect evidence• Maybe someday soon we’ll be able to test with
sensitive brain imaging
Adjacent maxicolumns in phonological cortex?
ge- ke-
be- pe-
te- de-A module of six
contiguous maxicolumns
Each of these maxicolumns is
divided into functional columns
Note that the entire module responds to [-e-]
Hypercolum
Adjacent maxicolumns in phonological cortex?
ge- ke-
be- pe-
te- de-A module of six
contiguous maxicolumns
The entire module responds to [-e-]
deb
ded den de- det del
dek
The entire maxicolumn responds to [de-]
Learning phonological distinctions:A hypothesis
ge- ke-
be- pe-
te- de-1. In learning,
this hypercolumn
gets established
first, responding to
[-e-]2. It gets subdivided into maxicolumns for demisyllables
deb
ded den de- det del
dek
3. The maxicolumn gets divided into functional columns
Indirect evidence for the hypothesis
Fits the structural organization demonstrated in monkey vision
Cortical structure and function have a high degree of uniformity
MEG is able to pick up different locations in Wernicke’s area for different vowels• MEG can only detect activity of at least 10,000
contiguous apical dendrites (Papanicolaou) Requires perhaps at least 250 adjacent minicolumns The size of a maxicolumn or hypercolumn
Remaining question: The process of learning distinctions
When a hypercolumn is first recruited, no lateral inhibition among its internal subdivisions
Later, when finer distinctions are learned, they get reinforced by lateral inhibition
Question: How does this work?
Inhibitory connections Based on Mountcastle (1998)
Columnar specificity is maintained by pericolumnar inhibition (190)
• Activity in one column can suppress that in its immediate neighbors (191)
Inhibitory cells can also inhibit other inhibitory cells (193)
Inhibitory cells can connect to axons of other cells (“axoaxonal connections”)
Large basket cells send myelinated projections as far as 1-2 mm horizontally (193)
Neural processes for learning
Basic principle: when a connection is successfully used, it becomes stronger• Successfully used if another connection to same
node is simultaneously active Mechanisms of strengthening
• Biochemical changes at synapses• Growth of dendritic spines• Formation of new synapses
Weakening: when neurons fire independently of each other their mutual connections (if any) weaken
Neural processes for learning
A
B
C
If connections AC and BC are active at the same time, and if their joint activation is strong enough to activate C, they both get strengthened
(adapted from Hebb)
Synapses here get strengthened
end